Overview

Dataset statistics

Number of variables19
Number of observations7905
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory152.0 B

Variable types

Numeric11
Categorical5
Boolean3

Alerts

Sex is highly imbalanced (62.7%)Imbalance
Ascites is highly imbalanced (72.2%)Imbalance
Edema is highly imbalanced (65.7%)Imbalance

Reproduction

Analysis started2024-02-08 19:30:40.509701
Analysis finished2024-02-08 19:30:52.042012
Duration11.53 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

N_Days
Real number (ℝ)

Distinct461
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2030.1733
Minimum41
Maximum4795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2024-02-08T13:30:52.109346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile334
Q11230
median1831
Q32689
95-th percentile4127
Maximum4795
Range4754
Interquartile range (IQR)1459

Descriptive statistics

Standard deviation1094.2337
Coefficient of variation (CV)0.53898539
Kurtosis-0.49401726
Mean2030.1733
Median Absolute Deviation (MAD)724
Skewness0.44865975
Sum16048520
Variance1197347.5
MonotonicityNot monotonic
2024-02-08T13:30:52.222394image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1216 117
 
1.5%
1434 105
 
1.3%
769 83
 
1.0%
3445 73
 
0.9%
1765 64
 
0.8%
1785 64
 
0.8%
1363 60
 
0.8%
904 59
 
0.7%
334 58
 
0.7%
2294 56
 
0.7%
Other values (451) 7166
90.7%
ValueCountFrequency (%)
41 13
0.2%
51 16
0.2%
71 14
0.2%
76 1
 
< 0.1%
77 21
0.3%
78 1
 
< 0.1%
108 1
 
< 0.1%
110 25
0.3%
121 1
 
< 0.1%
124 1
 
< 0.1%
ValueCountFrequency (%)
4795 7
 
0.1%
4556 51
0.6%
4523 15
 
0.2%
4509 41
0.5%
4500 28
0.4%
4467 14
 
0.2%
4459 19
 
0.2%
4453 22
0.3%
4427 14
 
0.2%
4392 1
 
< 0.1%

Drug
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
Placebo
4010 
D-penicillamine
3895 

Length

Max length15
Median length7
Mean length10.941809
Min length7

Characters and Unicode

Total characters86495
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD-penicillamine
2nd rowPlacebo
3rd rowPlacebo
4th rowPlacebo
5th rowPlacebo

Common Values

ValueCountFrequency (%)
Placebo 4010
50.7%
D-penicillamine 3895
49.3%

Length

2024-02-08T13:30:52.330837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T13:30:52.417458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
placebo 4010
50.7%
d-penicillamine 3895
49.3%

Most occurring characters

ValueCountFrequency (%)
l 11800
13.6%
e 11800
13.6%
i 11685
13.5%
a 7905
9.1%
c 7905
9.1%
n 7790
9.0%
P 4010
 
4.6%
b 4010
 
4.6%
o 4010
 
4.6%
D 3895
 
4.5%
Other values (3) 11685
13.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74695
86.4%
Uppercase Letter 7905
 
9.1%
Dash Punctuation 3895
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 11800
15.8%
e 11800
15.8%
i 11685
15.6%
a 7905
10.6%
c 7905
10.6%
n 7790
10.4%
b 4010
 
5.4%
o 4010
 
5.4%
p 3895
 
5.2%
m 3895
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
P 4010
50.7%
D 3895
49.3%
Dash Punctuation
ValueCountFrequency (%)
- 3895
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 82600
95.5%
Common 3895
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 11800
14.3%
e 11800
14.3%
i 11685
14.1%
a 7905
9.6%
c 7905
9.6%
n 7790
9.4%
P 4010
 
4.9%
b 4010
 
4.9%
o 4010
 
4.9%
D 3895
 
4.7%
Other values (2) 7790
9.4%
Common
ValueCountFrequency (%)
- 3895
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 11800
13.6%
e 11800
13.6%
i 11685
13.5%
a 7905
9.1%
c 7905
9.1%
n 7790
9.0%
P 4010
 
4.6%
b 4010
 
4.6%
o 4010
 
4.6%
D 3895
 
4.5%
Other values (3) 11685
13.5%

Age
Real number (ℝ)

Distinct391
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18373.146
Minimum9598
Maximum28650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2024-02-08T13:30:52.510530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum9598
5-th percentile12307
Q115574
median18713
Q320684
95-th percentile24622
Maximum28650
Range19052
Interquartile range (IQR)5110

Descriptive statistics

Standard deviation3679.9587
Coefficient of variation (CV)0.20029007
Kurtosis-0.49738238
Mean18373.146
Median Absolute Deviation (MAD)2604
Skewness0.084091298
Sum1.4523972 × 108
Variance13542096
MonotonicityNot monotonic
2024-02-08T13:30:52.625910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22369 79
 
1.0%
22388 71
 
0.9%
20684 71
 
0.9%
19060 70
 
0.9%
16279 66
 
0.8%
20459 65
 
0.8%
19246 62
 
0.8%
14161 62
 
0.8%
22960 61
 
0.8%
23331 61
 
0.8%
Other values (381) 7237
91.5%
ValueCountFrequency (%)
9598 18
0.2%
10550 17
0.2%
10795 7
 
0.1%
10810 1
 
< 0.1%
10958 1
 
< 0.1%
11058 33
0.4%
11167 10
 
0.1%
11273 19
0.2%
11330 1
 
< 0.1%
11462 19
0.2%
ValueCountFrequency (%)
28650 36
0.5%
28018 5
 
0.1%
27398 22
0.3%
27394 1
 
< 0.1%
27239 1
 
< 0.1%
27220 23
0.3%
26580 8
 
0.1%
26567 1
 
< 0.1%
26259 13
 
0.2%
25899 20
0.3%

Sex
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
F
7336 
M
 
569

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7905
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 7336
92.8%
M 569
 
7.2%

Length

2024-02-08T13:30:52.739287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T13:30:52.819074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
f 7336
92.8%
m 569
 
7.2%

Most occurring characters

ValueCountFrequency (%)
F 7336
92.8%
M 569
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7905
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 7336
92.8%
M 569
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 7905
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 7336
92.8%
M 569
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 7336
92.8%
M 569
 
7.2%

Ascites
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
False
7525 
True
 
380
ValueCountFrequency (%)
False 7525
95.2%
True 380
 
4.8%
2024-02-08T13:30:52.893733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
True
4042 
False
3863 
ValueCountFrequency (%)
True 4042
51.1%
False 3863
48.9%
2024-02-08T13:30:52.969512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Spiders
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
False
5966 
True
1939 
ValueCountFrequency (%)
False 5966
75.5%
True 1939
 
24.5%
2024-02-08T13:30:53.043172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Edema
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
N
7161 
S
 
399
Y
 
345

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7905
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowY
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 7161
90.6%
S 399
 
5.0%
Y 345
 
4.4%

Length

2024-02-08T13:30:53.124798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T13:30:53.202792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
n 7161
90.6%
s 399
 
5.0%
y 345
 
4.4%

Most occurring characters

ValueCountFrequency (%)
N 7161
90.6%
S 399
 
5.0%
Y 345
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7905
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 7161
90.6%
S 399
 
5.0%
Y 345
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 7905
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 7161
90.6%
S 399
 
5.0%
Y 345
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 7161
90.6%
S 399
 
5.0%
Y 345
 
4.4%

Bilirubin
Real number (ℝ)

Distinct111
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5944845
Minimum0.3
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2024-02-08T13:30:53.293109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.5
Q10.7
median1.1
Q33
95-th percentile11
Maximum28
Range27.7
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation3.8129603
Coefficient of variation (CV)1.4696408
Kurtosis12.908824
Mean2.5944845
Median Absolute Deviation (MAD)0.5
Skewness3.3396953
Sum20509.4
Variance14.538666
MonotonicityNot monotonic
2024-02-08T13:30:53.398988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 847
 
10.7%
0.7 653
 
8.3%
0.8 613
 
7.8%
0.9 608
 
7.7%
0.5 552
 
7.0%
1.1 443
 
5.6%
1.3 368
 
4.7%
1 292
 
3.7%
0.4 180
 
2.3%
1.4 175
 
2.2%
Other values (101) 3174
40.2%
ValueCountFrequency (%)
0.3 52
 
0.7%
0.4 180
 
2.3%
0.5 552
7.0%
0.6 847
10.7%
0.7 653
8.3%
0.8 613
7.8%
0.9 608
7.7%
1 292
 
3.7%
1.1 443
5.6%
1.2 166
 
2.1%
ValueCountFrequency (%)
28 13
0.2%
25.5 13
0.2%
24.5 16
0.2%
22.5 16
0.2%
21.9 1
 
< 0.1%
21.6 19
0.2%
21.4 1
 
< 0.1%
20 4
 
0.1%
18 4
 
0.1%
17.9 9
0.1%

Cholesterol
Real number (ℝ)

Distinct226
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean350.56192
Minimum120
Maximum1775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2024-02-08T13:30:53.503580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile198
Q1248
median298
Q3390
95-th percentile646
Maximum1775
Range1655
Interquartile range (IQR)142

Descriptive statistics

Standard deviation195.37934
Coefficient of variation (CV)0.5573319
Kurtosis18.162327
Mean350.56192
Median Absolute Deviation (MAD)62
Skewness3.6796575
Sum2771192
Variance38173.088
MonotonicityNot monotonic
2024-02-08T13:30:53.613227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
448 152
 
1.9%
248 151
 
1.9%
263 143
 
1.8%
298 138
 
1.7%
232 131
 
1.7%
260 120
 
1.5%
257 117
 
1.5%
316 110
 
1.4%
236 109
 
1.4%
280 106
 
1.3%
Other values (216) 6628
83.8%
ValueCountFrequency (%)
120 10
 
0.1%
127 18
 
0.2%
132 36
0.5%
134 1
 
< 0.1%
149 7
 
0.1%
151 9
 
0.1%
168 9
 
0.1%
172 19
 
0.2%
174 20
 
0.3%
175 58
0.7%
ValueCountFrequency (%)
1775 11
0.1%
1712 19
0.2%
1600 22
0.3%
1492 1
 
< 0.1%
1480 11
0.1%
1436 1
 
< 0.1%
1336 9
0.1%
1276 21
0.3%
1236 1
 
< 0.1%
1128 14
0.2%

Albumin
Real number (ℝ)

Distinct160
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5483226
Minimum1.96
Maximum4.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2024-02-08T13:30:53.726027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.96
5-th percentile2.97
Q13.35
median3.58
Q33.77
95-th percentile4.08
Maximum4.64
Range2.68
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.34617081
Coefficient of variation (CV)0.097559002
Kurtosis1.3396217
Mean3.5483226
Median Absolute Deviation (MAD)0.21
Skewness-0.5611495
Sum28049.49
Variance0.11983423
MonotonicityNot monotonic
2024-02-08T13:30:54.142584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.35 370
 
4.7%
3.6 368
 
4.7%
3.7 326
 
4.1%
3.85 255
 
3.2%
3.5 223
 
2.8%
3.77 217
 
2.7%
3.26 195
 
2.5%
3.65 183
 
2.3%
3.61 166
 
2.1%
3.2 161
 
2.0%
Other values (150) 5441
68.8%
ValueCountFrequency (%)
1.96 4
 
0.1%
2.1 4
 
0.1%
2.23 3
 
< 0.1%
2.27 4
 
0.1%
2.31 4
 
0.1%
2.33 16
 
0.2%
2.35 1
 
< 0.1%
2.43 50
0.6%
2.52 1
 
< 0.1%
2.53 9
 
0.1%
ValueCountFrequency (%)
4.64 20
0.3%
4.52 5
 
0.1%
4.4 14
 
0.2%
4.38 24
0.3%
4.34 1
 
< 0.1%
4.31 1
 
< 0.1%
4.3 42
0.5%
4.26 1
 
< 0.1%
4.24 12
 
0.2%
4.23 19
0.2%

Copper
Real number (ℝ)

Distinct171
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.902846
Minimum4
Maximum588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2024-02-08T13:30:54.252896image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile14
Q139
median63
Q3102
95-th percentile231
Maximum588
Range584
Interquartile range (IQR)63

Descriptive statistics

Standard deviation75.899266
Coefficient of variation (CV)0.90460895
Kurtosis10.21299
Mean83.902846
Median Absolute Deviation (MAD)26
Skewness2.7017358
Sum663252
Variance5760.6986
MonotonicityNot monotonic
2024-02-08T13:30:54.364983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67 311
 
3.9%
52 303
 
3.8%
39 216
 
2.7%
58 207
 
2.6%
75 188
 
2.4%
41 179
 
2.3%
13 172
 
2.2%
20 169
 
2.1%
44 154
 
1.9%
38 151
 
1.9%
Other values (161) 5855
74.1%
ValueCountFrequency (%)
4 12
 
0.2%
5 2
 
< 0.1%
9 53
 
0.7%
10 25
 
0.3%
11 60
 
0.8%
12 36
 
0.5%
13 172
2.2%
14 42
 
0.5%
15 11
 
0.1%
16 7
 
0.1%
ValueCountFrequency (%)
588 19
0.2%
558 7
 
0.1%
464 26
0.3%
456 1
 
< 0.1%
444 21
0.3%
412 13
 
0.2%
380 43
0.5%
358 21
0.3%
308 4
 
0.1%
290 20
0.3%

Alk_Phos
Real number (ℝ)

Distinct364
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1816.7452
Minimum289
Maximum13862.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2024-02-08T13:30:54.480556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum289
5-th percentile614
Q1834
median1181
Q31857
95-th percentile6064.8
Maximum13862.4
Range13573.4
Interquartile range (IQR)1023

Descriptive statistics

Standard deviation1903.7507
Coefficient of variation (CV)1.0478908
Kurtosis11.59975
Mean1816.7452
Median Absolute Deviation (MAD)460
Skewness3.1955577
Sum14361371
Variance3624266.6
MonotonicityNot monotonic
2024-02-08T13:30:54.603052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
663 117
 
1.5%
1345 81
 
1.0%
7277 79
 
1.0%
944 78
 
1.0%
794 76
 
1.0%
645 76
 
1.0%
1636 76
 
1.0%
1052 75
 
0.9%
2276 63
 
0.8%
674 63
 
0.8%
Other values (354) 7121
90.1%
ValueCountFrequency (%)
289 32
0.4%
310 10
 
0.1%
369 21
0.3%
377 17
0.2%
414 8
 
0.1%
423 31
0.4%
453 26
0.3%
466 16
0.2%
516 12
 
0.2%
554 31
0.4%
ValueCountFrequency (%)
13862.4 15
0.2%
13486.2 1
 
< 0.1%
12258.8 26
0.3%
11552 11
0.1%
11320.2 15
0.2%
11046.6 12
0.2%
10795.4 1
 
< 0.1%
10396.8 22
0.3%
10165 11
0.1%
9933.2 3
 
< 0.1%

SGOT
Real number (ℝ)

Distinct206
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.6046
Minimum26.35
Maximum457.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2024-02-08T13:30:54.714482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum26.35
5-th percentile54.25
Q175.95
median108.5
Q3137.95
95-th percentile198.4
Maximum457.25
Range430.9
Interquartile range (IQR)62

Descriptive statistics

Standard deviation48.790945
Coefficient of variation (CV)0.42573286
Kurtosis5.8167874
Mean114.6046
Median Absolute Deviation (MAD)31
Skewness1.5348057
Sum905949.38
Variance2380.5563
MonotonicityNot monotonic
2024-02-08T13:30:54.820900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.3 256
 
3.2%
57.35 247
 
3.1%
137.95 206
 
2.6%
120.9 198
 
2.5%
97.65 189
 
2.4%
170.5 184
 
2.3%
93 178
 
2.3%
128.65 170
 
2.2%
66.65 138
 
1.7%
106.95 137
 
1.7%
Other values (196) 6002
75.9%
ValueCountFrequency (%)
26.35 8
 
0.1%
28.38 12
 
0.2%
40.6 1
 
< 0.1%
41.85 16
 
0.2%
43.4 40
0.5%
45 14
 
0.2%
46.5 6
 
0.1%
49.6 52
0.7%
51.15 57
0.7%
52 15
 
0.2%
ValueCountFrequency (%)
457.25 17
0.2%
338 9
0.1%
328.6 15
0.2%
299.15 6
 
0.1%
288 9
0.1%
280.55 15
0.2%
272.8 9
0.1%
260.15 1
 
< 0.1%
253 1
 
< 0.1%
246.45 13
0.2%

Tryglicerides
Real number (ℝ)

Distinct154
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.34016
Minimum33
Maximum598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2024-02-08T13:30:54.926039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile56
Q184
median104
Q3139
95-th percentile210
Maximum598
Range565
Interquartile range (IQR)55

Descriptive statistics

Standard deviation52.530402
Coefficient of variation (CV)0.45543894
Kurtosis15.048118
Mean115.34016
Median Absolute Deviation (MAD)27
Skewness2.6339208
Sum911764
Variance2759.4431
MonotonicityNot monotonic
2024-02-08T13:30:55.033727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 262
 
3.3%
85 223
 
2.8%
91 218
 
2.8%
118 211
 
2.7%
68 188
 
2.4%
56 187
 
2.4%
146 181
 
2.3%
108 175
 
2.2%
55 171
 
2.2%
133 170
 
2.2%
Other values (144) 5919
74.9%
ValueCountFrequency (%)
33 13
 
0.2%
44 37
 
0.5%
46 12
 
0.2%
49 13
 
0.2%
50 19
 
0.2%
52 24
 
0.3%
53 15
 
0.2%
55 171
2.2%
56 187
2.4%
57 10
 
0.1%
ValueCountFrequency (%)
598 13
0.2%
432 16
0.2%
393 1
 
< 0.1%
382 4
 
0.1%
322 5
 
0.1%
319 15
0.2%
318 18
0.2%
309 20
0.3%
283 1
 
< 0.1%
280 20
0.3%

Platelets
Real number (ℝ)

Distinct227
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265.22897
Minimum62
Maximum563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2024-02-08T13:30:55.144747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile128
Q1211
median265
Q3316
95-th percentile430
Maximum563
Range501
Interquartile range (IQR)105

Descriptive statistics

Standard deviation87.465579
Coefficient of variation (CV)0.32977385
Kurtosis0.33057783
Mean265.22897
Median Absolute Deviation (MAD)53
Skewness0.42004793
Sum2096635
Variance7650.2274
MonotonicityNot monotonic
2024-02-08T13:30:55.258005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
344 233
 
2.9%
228 159
 
2.0%
268 158
 
2.0%
295 154
 
1.9%
336 147
 
1.9%
251 144
 
1.8%
265 138
 
1.7%
269 136
 
1.7%
213 136
 
1.7%
309 132
 
1.7%
Other values (217) 6368
80.6%
ValueCountFrequency (%)
62 11
 
0.1%
65 1
 
< 0.1%
70 10
 
0.1%
71 15
 
0.2%
76 1
 
< 0.1%
79 18
0.2%
80 25
0.3%
81 11
 
0.1%
88 3
 
< 0.1%
95 38
0.5%
ValueCountFrequency (%)
563 36
0.5%
539 5
 
0.1%
518 14
 
0.2%
515 2
 
< 0.1%
514 13
 
0.2%
493 17
0.2%
487 10
 
0.1%
474 17
0.2%
471 24
0.3%
467 40
0.5%

Prothrombin
Real number (ℝ)

Distinct49
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.629462
Minimum9
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.9 KiB
2024-02-08T13:30:55.368685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile9.6
Q110
median10.6
Q311
95-th percentile12
Maximum18
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.78173483
Coefficient of variation (CV)0.073544155
Kurtosis4.288955
Mean10.629462
Median Absolute Deviation (MAD)0.5
Skewness1.292436
Sum84025.9
Variance0.61110934
MonotonicityNot monotonic
2024-02-08T13:30:55.479021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
10.6 1070
 
13.5%
11 842
 
10.7%
10 638
 
8.1%
9.9 517
 
6.5%
9.8 440
 
5.6%
10.1 390
 
4.9%
10.9 339
 
4.3%
11.5 295
 
3.7%
9.6 288
 
3.6%
10.2 283
 
3.6%
Other values (39) 2803
35.5%
ValueCountFrequency (%)
9 8
 
0.1%
9.1 9
 
0.1%
9.2 5
 
0.1%
9.3 8
 
0.1%
9.4 17
 
0.2%
9.5 137
 
1.7%
9.6 288
3.6%
9.7 199
 
2.5%
9.8 440
5.6%
9.9 517
6.5%
ValueCountFrequency (%)
18 1
 
< 0.1%
17.1 2
 
< 0.1%
15.2 12
 
0.2%
14.1 4
 
0.1%
13.6 9
 
0.1%
13.4 1
 
< 0.1%
13.3 6
 
0.1%
13.2 32
0.4%
13.1 1
 
< 0.1%
13 45
0.6%

Stage
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
3.0
3153 
4.0
2703 
2.0
1652 
1.0
397 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters23715
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row4.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.0 3153
39.9%
4.0 2703
34.2%
2.0 1652
20.9%
1.0 397
 
5.0%

Length

2024-02-08T13:30:55.575804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T13:30:55.659228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 3153
39.9%
4.0 2703
34.2%
2.0 1652
20.9%
1.0 397
 
5.0%

Most occurring characters

ValueCountFrequency (%)
. 7905
33.3%
0 7905
33.3%
3 3153
 
13.3%
4 2703
 
11.4%
2 1652
 
7.0%
1 397
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15810
66.7%
Other Punctuation 7905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7905
50.0%
3 3153
 
19.9%
4 2703
 
17.1%
2 1652
 
10.4%
1 397
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 7905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 7905
33.3%
0 7905
33.3%
3 3153
 
13.3%
4 2703
 
11.4%
2 1652
 
7.0%
1 397
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 7905
33.3%
0 7905
33.3%
3 3153
 
13.3%
4 2703
 
11.4%
2 1652
 
7.0%
1 397
 
1.7%

Status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.9 KiB
C
4965 
D
2665 
CL
 
275

Length

Max length2
Median length1
Mean length1.0347881
Min length1

Characters and Unicode

Total characters8180
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowC
3rd rowD
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 4965
62.8%
D 2665
33.7%
CL 275
 
3.5%

Length

2024-02-08T13:30:55.751064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-08T13:30:55.830867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
c 4965
62.8%
d 2665
33.7%
cl 275
 
3.5%

Most occurring characters

ValueCountFrequency (%)
C 5240
64.1%
D 2665
32.6%
L 275
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8180
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 5240
64.1%
D 2665
32.6%
L 275
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 8180
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 5240
64.1%
D 2665
32.6%
L 275
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 5240
64.1%
D 2665
32.6%
L 275
 
3.4%

Interactions

2024-02-08T13:30:50.787699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:40.983377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:42.394779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:43.280456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:44.108767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:44.974042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:46.074379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:46.973540image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:47.869753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:48.719903image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:49.894025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:50.872315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:41.061526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:42.470064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:43.356100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:44.185991image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:45.281082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:46.154179image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:47.055857image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:47.942547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:48.805361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:49.971355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:50.952008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:41.150432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:42.551118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:43.436005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:44.267822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:45.362474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:46.239298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:47.143918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:48.024229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:48.894994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:50.055808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:51.028939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:41.229544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:42.627856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:43.509130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:44.346019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:45.438098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:46.316073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:47.224843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:48.095982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:49.224126image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:50.153343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:51.108105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:41.309561image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:42.708062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:43.587739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:44.424699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:45.515734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:46.397077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:47.307831image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:48.173036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:49.306734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:50.247661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:51.184630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:41.386603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:42.783915image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:43.663131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:44.502980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:45.609569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:46.477263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:47.390220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:48.247648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:49.393528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:50.332534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:51.262834image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:41.469561image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:42.866309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:43.740331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:44.582453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:45.690198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:46.558433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:47.472265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:48.322120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:49.485531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:50.413072image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:51.343143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:41.551413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:42.949746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:43.821031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:44.664588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:45.774090image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:46.643630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:47.558105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:48.408001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:49.577252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:50.494813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:51.416064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:41.620801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:43.036301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:43.890235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:44.736878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:45.846288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:46.722987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:47.631030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:48.488342image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:49.657576image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:50.565568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:51.534884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:42.251889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:43.116480image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:43.964556image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:44.815525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:45.923624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:46.815527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:47.713888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:48.569417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:49.740739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:50.639645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:51.615605image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:42.322096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:43.199838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:44.036783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:44.893528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:45.998944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:46.894332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:47.790340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:48.642967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:49.818950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-08T13:30:50.713254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-02-08T13:30:51.735324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-08T13:30:51.946056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

N_DaysDrugAgeSexAscitesHepatomegalySpidersEdemaBilirubinCholesterolAlbuminCopperAlk_PhosSGOTTrygliceridesPlateletsProthrombinStageStatus
0999D-penicillamine21532MNNNN2.3316.03.35172.01601.0179.8063.0394.09.73.0D
12574Placebo19237FNNNN0.9364.03.5463.01440.0134.8588.0361.011.03.0C
23428Placebo13727FNYYY3.3299.03.55131.01029.0119.3550.0199.011.74.0D
32576Placebo18460FNNNN0.6256.03.5058.01653.071.3096.0269.010.73.0C
4788Placebo16658FNYNN1.1346.03.6563.01181.0125.5596.0298.010.64.0C
5703D-penicillamine19270FNYNN0.6227.03.4634.06456.260.6368.0213.011.53.0D
61300Placebo17703FNNNN1.0328.03.3543.01677.0137.9590.0291.09.83.0C
71615Placebo21281FNYNN0.6273.03.9436.0598.052.70214.0227.09.93.0C
82050D-penicillamine20684FNNNN0.7360.03.6572.03196.094.55154.0269.09.82.0C
92615D-penicillamine15009FNNNN0.9478.03.6039.01758.0171.00140.0234.010.62.0C
N_DaysDrugAgeSexAscitesHepatomegalySpidersEdemaBilirubinCholesterolAlbuminCopperAlk_PhosSGOTTrygliceridesPlateletsProthrombinStageStatus
78951433Placebo14161FNNNN0.5291.04.2437.01065.085.25195.0201.010.62.0C
78961271Placebo13806FNNNN0.6328.03.9531.0663.052.70166.0344.010.43.0C
78971455Placebo16898FNNYN3.4279.03.53143.0671.0113.1572.0151.09.83.0C
789877Placebo19884FYYNY5.1178.02.75464.01020.0120.90118.080.012.34.0D
78991413Placebo24622FNNNN1.3262.03.7365.02045.089.9078.0181.011.03.0D
79001166D-penicillamine16839FNNNN0.8309.03.5638.01629.079.05224.0344.09.92.0C
79011492Placebo17031FNYNN0.9260.03.4362.01440.0142.0078.0277.010.04.0C
79021576D-penicillamine25873FNNYS2.0225.03.1951.0933.069.7562.0200.012.72.0D
79033584D-penicillamine22960MNYNN0.7248.02.7532.01003.057.35118.0221.010.64.0D
79041978D-penicillamine19237FNNNN0.7256.03.2322.0645.074.4085.0336.010.33.0C